After a stroke, approximately one-third of patients suffer from aphasia, a language disorder that impairs communication ability. The standard behavioral tests used to diagnose aphasia are time-consuming, require subjective interpretation, and have low ecological validity. As a consequence, comorbid cognitive problems present in individuals with aphasia can bias test results, generating a discrepancy between test outcomes and everyday-life language abilities. Neural tracking of the speech envelope is a promising tool for investigating brain responses to natural speech. The envelope of speech is crucial for speech understanding, encompassing cues for detecting and segmenting linguistic units, e.g., phrases, words and phonemes. In this study, we aimed to test the potential of the neural envelope tracking technique for detecting language impairments in aphasia. We recorded EEG from 27 individuals with aphasia in the chronic phase after stroke and 22 healthy controls while they listened to a 25-minute story. We quantified neural envelope tracking in a broadband frequency range as well as in the delta, theta, alpha, beta, and gamma frequency bands using mutual information analysis. Besides group differences in neural tracking measures, we also tested its suitability for detecting aphasia at the individual level using a support vector machine classifier. We further investigated the required recording length for the classifier to detect aphasia and to obtain reliable outcomes. Individuals with aphasia displayed decreased neural envelope tracking compared to healthy controls in the broad, delta, theta, and gamma band, which is in line with the assumed role of these bands in auditory and linguistic processing of speech. Neural tracking in these frequency bands effectively captured aphasia at the individual level, with a classification accuracy of 84% and an area under the curve of 88%. Moreover, we demonstrated that high-accuracy detection of aphasia can be achieved in a time-efficient (5-7 minutes) and highly reliable manner (split-half reliability correlations between R=0.62 and R=0.96 across frequency bands). Our study shows that neural envelope tracking of natural speech is an effective biomarker for language impairments in post-stroke aphasia. We demonstrated its potential as a diagnostic tool with high reliability, individual-level detection of aphasia, and time-efficient assessment. This work represents a significant step towards more automatic, objective, and ecologically valid assessments of language impairments in aphasia.

Objective. The human brain tracks the temporal envelope of speech, which contains essential cues for speech understanding. Linear models are the most common tool to study neural envelope tracking. However, information on how speech is processed can be lost since nonlinear relations are precluded. Analysis based on mutual information (MI), on the other hand, can detect both linear and nonlinear relations and is gradually becoming more popular in the field of neural envelope tracking. Yet, several different approaches to calculating MI are applied with no consensus on which approach to use. Furthermore, the added value of nonlinear techniques remains a subject of debate in the field. The present paper aims to resolve these open questions. Approach. We analyzed electroencephalography (EEG) data of participants listening to continuous speech and applied MI analyses and linear models. Main results. Comparing the different MI approaches, we conclude that results are most reliable and robust using the Gaussian copula approach, which first transforms the data to standard Gaussians. With this approach, the MI analysis is a valid technique for studying neural envelope tracking. Like linear models, it allows spatial and temporal interpretations of speech processing, peak latency analyses, and applications to multiple EEG channels combined. In a final analysis, we tested whether nonlinear components were present in the neural response to the envelope by first removing all linear components in the data. We robustly detected nonlinear components on the single-subject level using the MI analysis. Significance. We demonstrate that the human brain is nonlinearly related to the temporal envelope of speech. Unlike linear models, the MI analysis detects such nonlinear relations, proving its added value to neural envelope tracking. In addition, the MI analysis retains spatial and temporal characteristics of speech processing, an advantage lost when using more complex (nonlinear) deep neural networks.

Aphasia is a common consequence of a stroke which affects language processing. In search of an objective biomarker for aphasia, we used EEG to investigate how functional network patterns in the cortex are affected in persons with post-stroke chronic aphasia (PWA) compared to healthy controls (HC) while they are listening to a story. EEG was recorded from 22 HC and 27 PWA while they listened to a 25-min-long story. Functional connectivity between scalp regions was measured with the weighted phase lag index. The Network-Based Statistics toolbox was used to detect altered network patterns and to investigate correlations with behavioural tests within the aphasia group. Differences in network geometry were assessed by means of graph theory and a targeted node-attack approach. Group-classification accuracy was obtained with a support vector machine classifier. PWA showed stronger inter-hemispheric connectivity compared to HC in the theta-band (4.5-7 Hz), whilst a weaker subnetwork emerged in the low-gamma band (30.5-49 Hz). Two subnetworks correlated with semantic fluency in PWA respectively in delta- (1-4 Hz) and low-gamma-bands. In the theta-band network, graph alterations in PWA emerged at both local and global level, whilst only local changes were found in the low-gamma-band network. As assessed with the targeted node-attack, PWA exhibit a more scale-free network compared to HC. Network metrics effectively discriminated PWA and HC (AUC = 83%). Overall, we showed for that EEG-network metrics are effective biomarkers to assess natural speech processing in chronic aphasia. We hypothesize that the detected alterations reflect compensatory mechanisms associated with recovery.

The human brain tracks the temporal envelope of speech, which contains essential cues for speech understanding. Linear models are the most common tool to study neural envelope tracking. However, information on how speech is processed can be lost since nonlinear relations are precluded. As an alternative, mutual information (MI) analysis can detect both linear and nonlinear relations. Yet, several different approaches to calculating MI are applied without consensus on which approach to use. Furthermore, the added value of nonlinear techniques remains a subject of debate in the field. To resolve this, we applied linear and MI analyses to electroencephalography (EEG) data of participants listening to continuous speech. Comparing the different MI approaches, we conclude that results are most reliable and robust using the Gaussian copula approach, which first transforms the data to standard Gaussians. With this approach, the MI analysis is a valid technique for studying neural envelope tracking. Like linear models, it allows spatial and temporal interpretations of speech processing, peak latency analyses, and applications to multiple EEG channels combined. Finally, we demonstrate that the MI analysis can detect nonlinear components on the single-subject level, beyond the limits of linear models. We conclude that the MI analysis is a more informative tool for studying neural envelope tracking.